Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration

TL;DR

This paper introduces a method to learn action durations and the synergy between human and robot actions in collaborative tasks, improving task planning by understanding interaction effects and optimizing safety and efficiency.

Contribution

It presents a novel approach to learning action costs and coupling effects from past executions, enabling better task planning in human-robot collaboration scenarios.

Findings

The method accurately learns action durations from past data.

It identifies negative synergies where actions slow each other down.

The approach improves planning by accounting for interaction effects.

Abstract

A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans and robots. We leverage the information from past executions to learn the average duration of each action and a synergy coefficient representing the effect of an action performed by the human on the duration of the action performed by the robot (and vice versa). We implement the proposed method in a simulated scenario where both agents can access the same area simultaneously. Safety measures require the robot to slow down when the human is close, denoting a bad synergy of tasks operating in the same area. We show that our approach can learn such bad couplings so that a task planner can leverage this information to find better plans.